Tensorflow Implementation of Pixel Transposed Convolutional Networks (PixelTCN and PixelTCL)
Branch: master
Clone or download
Latest commit 71964b6 Dec 13, 2018
Type Name Latest commit message Commit time
Failed to load latest commit information.
assets replace pdf by png May 25, 2017
dataset enlarge data May 29, 2017
utils Update pixel_dcn.py Mar 15, 2018
.gitignore remove main.py.sample to avoid fuzy May 25, 2017
LICENSE Create LICENSE Jul 9, 2017
README.md Update README.md Dec 13, 2018
_config.yml Update _config.yml May 28, 2017
main.py fix bug Aug 18, 2017
network.py modify mean iou Aug 24, 2017


Pixel Transposed Convolutional Networks

Created by Hongyang Gao, Hao Yuan, Zhengyang Wang and Shuiwang Ji at Texas A&M University.


Pixel transposed convolutional layer (PixelTCL) is a more effective way to perform up-sampling operations than transposed convolutional layer.

Detailed information about PixelTCL is provided in [arXiv tech report] (https://arxiv.org/abs/1705.06820).


If using this code, please cite our paper.

  title={Pixel Transposed Convolutional Networks},
  author={Hongyang Gao and Hao Yuan and Zhengyang Wang and Shuiwang Ji},
  journal={arXiv preprint arXiv:1705.06820},


Semantic segmentation


Comparison of semantic segmentation results. The first and second rows are images and ground true labels, respectively. The third and fourth rows are the results of using regular transposed convolution and our proposed pixel transposed convolution, respectively.

Generate real images (VAE)


Sample face images generated by VAEs when trained on the CelebA dataset. The first two rows are images generated by a standard VAE with transposed convolutional layers for up-sampling. The last two rows are images generated by the same VAE model, but using PixelTCL for up-sampling in the generator network.

System requirement

Programming language

Python 3.5+

Python Packages

tensorflow (CPU) or tensorflow-gpu (GPU), numpy, h5py, progressbar, PIL, scipy

Prepare data

In this project, we provided a set of sample datasets for training, validation, and testing. If want to train on other data such as PASCAL, prepare the h5 files as required. utils/h5_utils.py could be used to generate h5 files.

Configure the network

All network hyperparameters are configured in main.py.


max_step: how many iterations or steps to train

test_step: how many steps to perform a mini test or validation

save_step: how many steps to save the model

summary_step: how many steps to save the summary


data_dir: data directory

train_data: h5 file for training

valid_data: h5 file for validation

test_data: h5 file for testing

batch: batch size

channel: input image channel number

height, width: height and width of input image


logdir: where to store log

modeldir: where to store saved models

sampledir: where to store predicted samples, please add a / at the end for convinience

model_name: the name prefix of saved models

reload_step: where to return training

test_step: which step to test or predict

random_seed: random seed for tensorflow

Network architecture

network_depth: how deep of the U-Net including the bottom layer

class_num: how many classes. Usually number of classes plus one for background

start_channel_num: the number of channel for the first conv layer

conv_name: use which convolutional layer in decoder. We have conv2d for standard convolutional layer, and ipixel_cl for input pixel convolutional layer proposed in our paper.

deconv_name: use which upsampling layer in decoder. We have deconv for standard transposed convolutional layer, ipixel_dcl for input pixel transposed convolutional layer, and pixel_dcl for pixel transposed convolutional layer proposed in our paper.

Training and Testing

Start training

After configure the network, we can start to train. Run

python main.py

The training of a U-Net for semantic segmentation will start.

Training process visualization

We employ tensorboard to visualize the training process.

tensorboard --logdir=logdir/

The segmentation results including training and validation accuracies, and the prediction outputs are all available in tensorboard.

Testing and prediction

Select a good point to test your model based on validation or other measures.

Fill the test_step in main.py with the checkpoint you want to test, run

python main.py --action=test

The final output include accuracy and mean_iou.

If you want to make some predictions, run

python main.py --action=predict

The predicted segmentation results will be in sampledir set in main.py, colored.

Use PixelDCL in other models

If you want to use pixel transposed convolutional layer in other models, just copy the file


and use it in your model:

from pixel_dcn import pixel_dcl, ipixel_dcl, ipixel_cl

outputs = pixel_dcl(inputs, out_num, kernel_size, scope)

Currently, this version only support up-sampling by factor 2 such as from 2x2 to 4x4. We may provide more flexible version in the future.